Full-Body Musculoskeletal Model for Muscle-Driven Simulation of Human Gait

Objective: Musculoskeletal models provide a noninvasive means to study human movement and predict the effects of interventions on gait. Our goal was to create an open-source 3-D musculoskeletal model with high-fidelity representations of the lower limb musculature of healthy young individuals that c...

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Published inIEEE transactions on biomedical engineering Vol. 63; no. 10; pp. 2068 - 2079
Main Authors Rajagopal, Apoorva, Dembia, Christopher L., DeMers, Matthew S., Delp, Denny D., Hicks, Jennifer L., Delp, Scott L.
Format Journal Article
LanguageEnglish
Published United States IEEE 01.10.2016
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Online AccessGet full text
ISSN0018-9294
1558-2531
1558-2531
DOI10.1109/TBME.2016.2586891

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Abstract Objective: Musculoskeletal models provide a noninvasive means to study human movement and predict the effects of interventions on gait. Our goal was to create an open-source 3-D musculoskeletal model with high-fidelity representations of the lower limb musculature of healthy young individuals that can be used to generate accurate simulations of gait. Methods: Our model includes bony geometry for the full body, 37 degrees of freedom to define joint kinematics, Hill-type models of 80 muscle-tendon units actuating the lower limbs, and 17 ideal torque actuators driving the upper body. The model's musculotendon parameters are derived from previous anatomical measurements of 21 cadaver specimens and magnetic resonance images of 24 young healthy subjects. We tested the model by evaluating its computational time and accuracy of simulations of healthy walking and running. Results: Generating muscle-driven simulations of normal walking and running took approximately 10 minutes on a typical desktop computer. The differences between our muscle-generated and inverse dynamics joint moments were within 3% (RMSE) of the peak inverse dynamics joint moments in both walking and running, and our simulated muscle activity showed qualitative agreement with salient features from experimental electromyography data. Conclusion: These results suggest that our model is suitable for generating muscle-driven simulations of healthy gait. We encourage other researchers to further validate and apply the model to study other motions of the lower extremity. Significance: The model is implemented in the open-source software platform OpenSim. The model and data used to create and test the simulations are freely available at https://simtk.org/home/full_body/, allowing others to reproduce these results and create their own simulations.
AbstractList Musculoskeletal models provide a non-invasive means to study human movement and predict the effects of interventions on gait. Our goal was to create an open-source 3-D musculoskeletal model with high-fidelity representations of the lower limb musculature of healthy young individuals that can be used to generate accurate simulations of gait. Our model includes bony geometry for the full body, 37 degrees of freedom to define joint kinematics, Hill-type models of 80 muscle-tendon units actuating the lower limbs, and 17 ideal torque actuators driving the upper body. The model's musculotendon parameters are derived from previous anatomical measurements of 21 cadaver specimens and magnetic resonance images of 24 young healthy subjects. We tested the model by evaluating its computational time and accuracy of simulations of healthy walking and running. Generating muscle-driven simulations of normal walking and running took approximately 10 minutes on a typical desktop computer. The differences between our muscle-generated and inverse dynamics joint moments were within 3% (RMSE) of the peak inverse dynamics joint moments in both walking and running, and our simulated muscle activity showed qualitative agreement with salient features from experimental electromyography data. These results suggest that our model is suitable for generating muscle-driven simulations of healthy gait. We encourage other researchers to further validate and apply the model to study other motions of the lower extremity. The model is implemented in the open-source software platform OpenSim. The model and data used to create and test the simulations are freely available at https://simtk.org/home/full_body/, allowing others to reproduce these results and create their own simulations.
Musculoskeletal models provide a non-invasive means to study human movement and predict the effects of interventions on gait. Our goal was to create an open-source 3-D musculoskeletal model with high-fidelity representations of the lower limb musculature of healthy young individuals that can be used to generate accurate simulations of gait.OBJECTIVEMusculoskeletal models provide a non-invasive means to study human movement and predict the effects of interventions on gait. Our goal was to create an open-source 3-D musculoskeletal model with high-fidelity representations of the lower limb musculature of healthy young individuals that can be used to generate accurate simulations of gait.Our model includes bony geometry for the full body, 37 degrees of freedom to define joint kinematics, Hill-type models of 80 muscle-tendon units actuating the lower limbs, and 17 ideal torque actuators driving the upper body. The model's musculotendon parameters are derived from previous anatomical measurements of 21 cadaver specimens and magnetic resonance images of 24 young healthy subjects. We tested the model by evaluating its computational time and accuracy of simulations of healthy walking and running.METHODSOur model includes bony geometry for the full body, 37 degrees of freedom to define joint kinematics, Hill-type models of 80 muscle-tendon units actuating the lower limbs, and 17 ideal torque actuators driving the upper body. The model's musculotendon parameters are derived from previous anatomical measurements of 21 cadaver specimens and magnetic resonance images of 24 young healthy subjects. We tested the model by evaluating its computational time and accuracy of simulations of healthy walking and running.Generating muscle-driven simulations of normal walking and running took approximately 10 minutes on a typical desktop computer. The differences between our muscle-generated and inverse dynamics joint moments were within 3% (RMSE) of the peak inverse dynamics joint moments in both walking and running, and our simulated muscle activity showed qualitative agreement with salient features from experimental electromyography data.RESULTSGenerating muscle-driven simulations of normal walking and running took approximately 10 minutes on a typical desktop computer. The differences between our muscle-generated and inverse dynamics joint moments were within 3% (RMSE) of the peak inverse dynamics joint moments in both walking and running, and our simulated muscle activity showed qualitative agreement with salient features from experimental electromyography data.These results suggest that our model is suitable for generating muscle-driven simulations of healthy gait. We encourage other researchers to further validate and apply the model to study other motions of the lower extremity.CONCLUSIONThese results suggest that our model is suitable for generating muscle-driven simulations of healthy gait. We encourage other researchers to further validate and apply the model to study other motions of the lower extremity.The model is implemented in the open-source software platform OpenSim. The model and data used to create and test the simulations are freely available at https://simtk.org/home/full_body/, allowing others to reproduce these results and create their own simulations.SIGNIFICANCEThe model is implemented in the open-source software platform OpenSim. The model and data used to create and test the simulations are freely available at https://simtk.org/home/full_body/, allowing others to reproduce these results and create their own simulations.
Objective: Musculoskeletal models provide a noninvasive means to study human movement and predict the effects of interventions on gait. Our goal was to create an open-source 3-D musculoskeletal model with high-fidelity representations of the lower limb musculature of healthy young individuals that can be used to generate accurate simulations of gait. Methods: Our model includes bony geometry for the full body, 37 degrees of freedom to define joint kinematics, Hill-type models of 80 muscle-tendon units actuating the lower limbs, and 17 ideal torque actuators driving the upper body. The model's musculotendon parameters are derived from previous anatomical measurements of 21 cadaver specimens and magnetic resonance images of 24 young healthy subjects. We tested the model by evaluating its computational time and accuracy of simulations of healthy walking and running. Results: Generating muscle-driven simulations of normal walking and running took approximately 10 minutes on a typical desktop computer. The differences between our muscle-generated and inverse dynamics joint moments were within 3% (RMSE) of the peak inverse dynamics joint moments in both walking and running, and our simulated muscle activity showed qualitative agreement with salient features from experimental electromyography data. Conclusion: These results suggest that our model is suitable for generating muscle-driven simulations of healthy gait. We encourage other researchers to further validate and apply the model to study other motions of the lower extremity. Significance: The model is implemented in the open-source software platform OpenSim. The model and data used to create and test the simulations are freely available at https://simtk.org/home/full_body/, allowing others to reproduce these results and create their own simulations.
Author Rajagopal, Apoorva
Delp, Scott L.
DeMers, Matthew S.
Delp, Denny D.
Dembia, Christopher L.
Hicks, Jennifer L.
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  surname: Rajagopal
  fullname: Rajagopal, Apoorva
  organization: Department of Mechanical Engineering, Stanford University
– sequence: 2
  givenname: Christopher L.
  surname: Dembia
  fullname: Dembia, Christopher L.
  organization: Department of Mechanical Engineering, Stanford University
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  givenname: Matthew S.
  surname: DeMers
  fullname: DeMers, Matthew S.
  organization: Department of Mechanical Engineering, Stanford University
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  givenname: Denny D.
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  surname: Delp
  fullname: Delp, Denny D.
  organization: Department of Mechanical Engineering, Stanford University
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  givenname: Jennifer L.
  surname: Hicks
  fullname: Hicks, Jennifer L.
  organization: Department of Bioengineering, Stanford University
– sequence: 6
  givenname: Scott L.
  surname: Delp
  fullname: Delp, Scott L.
  email: delp@stanford.edu
  organization: Departments of Bioengineering and Mechanical Engineering, Stanford University, Stanford, CA, USA
BackLink https://www.ncbi.nlm.nih.gov/pubmed/27392337$$D View this record in MEDLINE/PubMed
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  day: 01
PublicationDecade 2010
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PublicationTitle IEEE transactions on biomedical engineering
PublicationTitleAbbrev TBME
PublicationTitleAlternate IEEE Trans Biomed Eng
PublicationYear 2016
Publisher IEEE
Publisher_xml – name: IEEE
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Snippet Objective: Musculoskeletal models provide a noninvasive means to study human movement and predict the effects of interventions on gait. Our goal was to create...
Musculoskeletal models provide a non-invasive means to study human movement and predict the effects of interventions on gait. Our goal was to create an...
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SubjectTerms Biological system modeling
Biomechanical Phenomena
Biomechanics
Biomedical Engineering
Cadaver
Computational modeling
Computer Simulation
Data models
gait
Gait - physiology
Humans
Male
Models, Biological
Muscle, Skeletal - physiology
Muscles
musculoskeletal model
Pelvis
running
Running - physiology
simulation
Torque
walking
Walking - physiology
Whole Body Imaging
Title Full-Body Musculoskeletal Model for Muscle-Driven Simulation of Human Gait
URI https://ieeexplore.ieee.org/document/7505900
https://www.ncbi.nlm.nih.gov/pubmed/27392337
https://www.proquest.com/docview/1823032465
https://pubmed.ncbi.nlm.nih.gov/PMC5507211
Volume 63
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